1 Summary

Number of samples: 180
Groups: HIV- moderate cannabis, HIV- daily cannabis, HIV- naive to cannabis, HIV+ naive to cannabis, HIV+ daily cannabis, HIV+ moderate cannabis
Treatments: CBD, CBDpIL1B, IL1B, THC, THCpIL1B, Vehicle
HIV status: HIVn, HIVp
Cannabis use: moderate, daily, naive

2 Workflow Overview

We processed the RNA-seq data from monocyte-derived macrophages (MDMs) using the following pipeline:

3 DESeq2 Model

The differential expression model used:

\[ design(dds) ~ treatment + HIV status + cannabis \]

treatment: vehicle, IL1b, CBD, THC, CBD+IL1B, THC+IL1b
hiv_status: HIV- vs HIV+
cannabis: naive, moderate, daily

4 Summary Analyses

4.1 Volcano Plots

volcano_list[["cannabis_daily_vs_naive"]]

4.2 PCA Plots

using ntop=500 top features by variance

using ntop=500 top features by variance

using ntop=500 top features by variance

using ntop=500 top features by variance

4.3 Top Genes Tables

# DT::datatable(top20_list[["cannabis_daily_vs_naive"]], options = list(pageLength = 5, scrollX = TRUE), rownames = FALSE)

DT::datatable(top20_list[["cannabis_daily_vs_naive"]]|>
  dplyr::mutate(across(where(base::is.numeric), ~ round(.x, 20))),
          rownames = FALSE,
          extensions = 'Buttons',
          options = list(
            dom = 'Bfrtip',
            buttons = c('copy', 'csv', 'excel'),
            pageLength = 10
          ))

Note

Only genes with padj < 0.05 and |log2FC| > 1 are labeled in volcano plots.
PCA plots can be faceted by hiv_status to observe group separation.
Interactive tables allow scrolling and sorting top genes for each contrast.

4.4 Pathway enrichment analysis

Gene Ontology (GO) enrichment analysis was performed separately for upregulated and downregulated genes for each contrast. Genes with an adjusted p-value < 0.01 and absolute log₂ fold change > 2 were selected. Enrichment analysis was conducted using the clusterProfiler package with the Biological Process (BP) ontology, using all expressed genes as the background universe. Enrichment results were visualized using dot plots, and leading-edge genes contributing to each enriched pathway were extracted for downstream interpretation.
Dot plots showing Gene Ontology Biological Process enrichment for upregulated (left) and downregulated (right) genes across experimental contrasts. Genes were considered differentially expressed if they exhibited an adjusted p-value < 0.01 and an absolute log₂ fold change > 2. Dot size represents the number of genes contributing to each pathway, and color indicates the adjusted p-value. Enrichment analyses were performed using the full set of expressed genes as background. Pathways are shown separately for genes increased and decreased in expression to facilitate biological interpretation.

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4.5 Top Differentially Expressed Genes

Heatmap showing variance-stabilized expression (VST) of the top N differentially expressed genes for the indicated contrast. Genes were selected based on adjusted p-value and absolute log2 fold change from DESeq2 analysis. Rows represent genes (labeled by gene symbol), and columns represent samples. Expression values are row-scaled (z-score) to emphasize relative expression patterns. Samples are ordered by HIV status, cannabis exposure, and treatment condition.

4.6 Selected / Candidate Genes

Heatmap showing variance-stabilized expression (VST) of selected genes of interest across samples. Genes were chosen a priori based on biological relevance (e.g., HIV response, inflammation, cannabinoid signaling). Rows represent genes (gene symbols), and columns represent samples. Expression values are row-scaled to highlight relative differences across conditions. Samples are ordered by HIV status, cannabis exposure, and treatment.

5 Impact of HIV on inflammatory phenotype

5.1 Methods

Here we compare PWH vs PWoH within vehicle-treated samples and adjust fo cannabis use.

5.2 Differentially Expressed Genes

Volcano plot of DE genes between PWH and PWoH macrophages. TNF is the only significantly upregulated inflammatory gene (padj < 0.05).

5.3 Enrichment of inflammatory pathways in vehicle-treated macrophages from PWH

Over-representation analysis (ORA) of genes differentially expressed between PWH and PWoH macrophages under vehicle conditions highlights enrichment of pathways related to cytokine production, adaptive immune responses, and immune effector processes. Point size indicates the number of DE genes in each pathway, while color represents the significance of enrichment (-log10 adjusted p-value)

5.4 GSEA of inflammatory pathways in vehicle-treated macrophages.

Ridgeplot from GSEA showing the distribution of pathway genes across the ranked gene list; peaks to the right indicate upregulation in PWH

Dot size indicates pathway size (number of genes). NES = normalized enrichment score; positive values indicate upregulation in PWH. Red dots indicate pathways where TNF is part of the core enrichment contributing to the NES. All three pathways are significantly enriched (NES 2.17–2.23, p.adjust < 0.01).

6 Modulation of IL1-B response by Cannabis Use

Important

IL1B induces a strong inflammatory response in PWH naive to cannabis, with upregulation of classic cytokine and immune effector genes (e.g., IL1B, IL6, TNFAIP6). Moderate and daily cannabis use appears to dampen this response, as most genes are not significantly induced in these groups. This suggests cannabis exposure may modulate macrophage inflammatory activation.

6.1 Method

6.1.1 Model rationale

The interaction framework allows us to distinguish:

Main effect of cannabis: baseline differences in gene expression between cannabis exposure groups
Main effect of treatment: transcriptional changes induced by IL1B stimulation
Interaction effect: whether the effect of IL1B stimulation differs depending on cannabis exposure. The interaction term specifically tests whether the IL1B-induced transcriptional response is modified by cannabis use, rather than assuming identical treatment effects across exposure groups.

6.1.2 Statistical model

For each gene (i), counts were modeled using a negative binomial distribution:

\[ log(μi)=β0+β1⋅Cannabis+β2⋅Treatment+β3⋅(Cannabis×Treatment) \]

Where:

μi is the expected expression of gene (i)
β0 is the intercept
β1 represents the main effect of cannabis exposure
β2 represents the main effect of IL1B treatment
β3 represents the interaction effect, capturing differential IL1B responses by cannabis status

6.1.3 Approach

RNA-seq data from MDM of PWH are analyzed using DESeq2. Genes responsive to IL1B stimulation are filtered to retain only those significant in at least one cannabis use condition (adjusted p < 0.05). Log2 fold-change (log2FC) values are extracted for naive, moderate, and daily cannabid exposure. Heatmaps were generated using ComplexHeatmap, with non-significant log2FC values desaturated and significant changes indicated with an asterisk (*). Key inflammatory genes (e.g., IL1B, TNF, CXCL10) were highlighted in bold. Interactive tables were produced with DT, combining log2FC values and significance for each condition.

6.2 Results

Heatmap showing log2 fold-change (log2FC) of IL1B-responsive inflammatory genes across three cannabis conditions: naive , moderate, and daily. Only genes significant in at least one condition (adjusted p < 0.05) are included. Log2FC values for non-significant conditions are shown but visually desaturated, while significant changes are highlighted with an asterisk (*). Blue, white, and red indicate negative, no change, and positive log2FC, respectively. Gray squares represent missing values (NA). Rows are clustered based on expression patterns. The log2FC color legend is shown on the right, and significance is indicated in a separate legend.

7 IL1B-induced inflammatory response modulated by CBD or THC

Here we compared inflammatory response triggered by IL-1B alone, IL-1B + THC and IL-B+CBD and adjusted for HIV status in persons Cannabis Naive.

7.1 1:1 Comparison IL-1B ± THC or CBD versus Vehicle

7.2 1:1 Comparison IL-1B modulation by THC or CBD

8 Cannabis * HIV Status Interaction models.

8.1 In HIV+ vs HIV- individuals

8.1.1 DESeq2 Model

The differential expression model used:

\[ design(dds_vehicle) <- ~ cannabis * hiv_status \]

8.1.2 Results

Important

Under vehicle conditions, TNF expression in macrophages from PWH was higher than in PWoH at the canonical transcript level (log₂FC ≈ +2.9), consistent with modest basal inflammatory activation. However, this difference did not reach statistical significance after multiple testing correction (FDR ≈ 1.0).

8.2 In HIV+ individuals, does moderate cannabis reduce TNF compared to naive?

8.2.1 DESeq2 Model

The differential expression model used:

\[ design(dds Vehicle HIVp) <- ~ cannabis \]

8.2.2 HIV+ moderate vs naive

8.2.3 HIV+ daily vs naive

8.2.4 HIV+ daily+moderate vs naive

Important

In HIV-positive macrophages under vehicle conditions, cannabis use was associated with a dose-dependent reduction in TNF expression, with moderate and daily users showing markedly lower TNF levels compared to cannabis-naive individuals (log₂FC −3.6 and −4.1, respectively; nominal p < 0.05).

8.3 TNF summary

8.3.1 Canonical TNF Transcripts

8.3.2 TNF expression in macrophages by HIV status and cannabis use.

Note

Normalized RNA-seq counts for all TNF transcripts (all ENSEMBL IDs annotated as TNF) were summed per sample and plotted as mean ± 95% confidence interval. PWH = people with HIV; PWoH = people without HIV. Bars show TNF expression under vehicle conditions stratified by cannabis use (naive, moderate, daily).

9 Gene set enrichment analysis (GSEA).

9.1 GSEA for Inflammatory pathway activation in HIV+ macrophages.

Tip

In vehicle-treated MDMs, are inflammatory pathways upregulated in HIV+ compared to HIV−, adjusting for cannabis use?

9.1.1 Method

Genes were ranked by log₂ fold change from DESeq2 differential expression analysis comparing HIV-positive to HIV-negative macrophages under vehicle conditions. GSEA was performed using the clusterProfiler package with Hallmark inflammatory gene sets from MSigDB. Statistical significance was assessed using permutation-based testing, and results are reported as normalized enrichment scores (NES) with Benjamini–Hochberg adjusted P values.

9.1.2 Interpretation

Gene set enrichment analysis revealed significant enrichment of TNFα signaling via NF-κB (NES = 1.92, adjusted P = 6.7×10⁻⁶) and a broader inflammatory response signature (NES = 1.47, adjusted P = 0.009) in HIV-positive macrophages relative to HIV-negative controls. Core enrichment genes included canonical inflammatory mediators such as TNF, IL1B, IL6, CXCL10, and NFKBIA, indicating coordinated activation of cytokine-driven inflammatory programs. NES stands for Normalized Enrichment Score..

Important

Vehicle-treated macrophages from PWH exhibited strong enrichment of TNFα/NFκB and inflammatory response pathways relative to HIV− controls, a signature that was attenuated in cannabis-exposed cells.

9.2 GSEA for cannabis effect within HIV+

Tip

Does cannabis dampen inflammatory signaling specifically in HIV+ MDMs? In other words, how cannabis moderate use changes gene expression specifically in HIV+ macrophages, relative to HIV+ naïve cannabis users.

Important

👉 TNFα / inflammatory genes are systematically downregulated in HIV+ MDMs with moderate cannabis use compared to HIV+ naïve users.
👉 Cannabis use dampens the basal inflammatory transcriptional program in HIV+ macrophages, particularly TNFα/NF-κB–driven signaling.

9.3 GSEA for interaction alone: Mechanistic ++

Tip

Are inflammatory pathways differentially regulated by cannabis in HIV+ vs HIV−? Is cannabis modulation different in HIV+ vs HIV−?

Important

👉 The suppressive effect of cannabis on inflammatory genes is significantly stronger in HIV+ macrophages than in HIV− macrophages.
👉 Cannabis dampens inflammatory signaling preferentially in HIV+ cells.

10 Are HIV-induced transcriptomic changes are attenuated (or altered) in cannabis users?

We explore the following contrasts:

Result	Meaning
`res_hiv_naive`	HIV effect in cannabis-naive
`res_hiv_users`	HIV effect in cannabis users
`res_interaction`	Difference between the two

10.1 HIV effect among cannabis-naive individuals

10.2 HIV effect among cannabis-users

10.3 Difference in HIV effect between cannabis users and naive

10.4 GSEA: Does cannabis attenuate HIV response?

10.4.1 Approach

Scatter plots show the log2 fold change of HIV-associated gene expression in cannabis-naive MDMs (x-axis) versus cannabis-using MDMs (y-axis). Each facet represents a selected Gene Ontology Biological Process (GO term) enriched for genes showing interaction effects between HIV and cannabis.

Points below the dashed identity line (y = x) indicate genes whose HIV effect is reduced in cannabis users compared with naive individuals (attenuation).
Points above the line indicate genes with enhanced HIV effect in users.
Red points highlight genes belonging to the specific GO term of the facet.
Gene labels indicate the top genes per GO term showing the largest reduction in HIV-induced expression (largest difference lfc_naive – lfc_users).

Quadrant	x = lfc_naive	y = lfc_users	Interpretation
`Top-right`	> 0	> 0	HIV upregulated in both groups
`Bottom-right`	> 0	< 0	HIV up in naive, down or attenuated in users
`Bottom-left`	< 0	< 0	HIV downregulated in both groups
`Top-left`	< 0	> 0	HIV down in naive, up in users (interaction effect)

10.4.2 Results for selected inflammatory genes

10.4.3 Results by GO terms

Genes falling below the identity line exhibit reduced HIV-associated induction in cannabis users compared with cannabis-naive individuals, consistent with attenuation of HIV-driven macrophage activation.
This visualization demonstrates that cannabis partially attenuates HIV-driven activation of immune and translational pathways, with most pathway genes falling below the identity line, highlighting a coherent dampening effect on macrophage activation.
Although cannabis use did not induce a global reversal of HIV-associated transcriptional changes, interaction-based GSEA revealed significant attenuation of innate immune and translational programs, suggesting a partial dampening of HIV-driven macrophage activation.